Generally, analysts manually use a set of tools to determine the most likely future state of a data set that is often described in a very qualitative manner. This can be a time-intensive task both in terms of algorithm development and execution. The general field of predictive analytics is often explored in this context to accomplish such a prediction.
Predictive analytics uses techniques such as regression models, discrete choice models, time series models, etc. with varying levels of success and timelines. For many of these techniques, a situation or collection of interrelated actors/objects is represented as a graph which has well-known ways of being characterized using scalars such as betweenness, centrality, cliques, etc. These graphs are defined as a set of nodes and edges. Many techniques utilize a known pattern of nodes and edges to compared directly with (portions of) the graph to determine a match and recognize a situation. In this approach, the recognition is on current state; there is no capability to match the past of the graph with the present or predict the future of the graph and the algorithm is far from real-time.
However, predictive analytics have at least one of the following shortcomings, e.g., lack scalability, do not consider multiple simultaneous models for evolving the graph, or are brittle, e.g., highly parameterized with hundreds or thousands of parameters.
Accordingly, there is a gap in meeting the current goals of analyzing large data sets. For example, one unmet goal involves the ability to automatically predict the evolution of data sets, e.g., situations, relationships, etc. A variety of techniques are currently employed or being developed that attempt to solve this problem. Some of these techniques include pattern recognition, traditional predictive analytics, evolution of macroscopic and microscopic characteristic scalar properties of network graphs, examination of Internet topology at different times, analysis using graph measures, temporal pattern matching at a node-level, maximum likelihood estimators, e.g., probabilistic predictions, and growing graphs by replicating existing structures and patterns.